{
  "abstract": "PurposeLumbar spine MRI interpretation requires 25 structured severity judgments per examination -- five degenerative conditions at five disc levels, each on a three-level ordinal scale -- a time-consuming task with substantial inter-reader variability. We developed and externally validated a model that automates this complete scheme.\n\nMaterials and MethodsA full-sequence multi-instance learning (MIL) model -- ResNet34 backbone, bidirectional GRU, attention pooling over 24 uniformly sampled sagittal T2-weighted slices -- with 25 cumulative-logit ordinal heads was trained on 1,204 examinations from the multi-institutional RSNA 2024 LumbarDISC dataset and internally validated on 213 held-out examinations. External validation used 60 retrospectively collected patients from an independent Chinese university hospital (four scanner vendors, radiology reports as the reference standard, no fine-tuning). Agreement was assessed with exact accuracy, sensitivity, specificity, McNemar tests, and patient-clustered bootstrap confidence intervals.\n\nResultsThe ordinal model achieved 51.4% balanced accuracy internally (exact accuracy 65.1%), versus 16.3% for a single-slice baseline; severe-grade AUCs averaged 0.908 across conditions. Externally, it outperformed its softmax-weighted counterpart on exact accuracy (0.649 vs. 0.586; +6.3 percentage points, 95% CI +2.7 to +10.0) and sensitivity (0.868 vs. 0.833), with markedly fewer severe-grade false alarms (69 vs. 172 predicted severe cells; McNemar p<0.0001).\n\nConclusionA full-sequence MIL model with ordinal outputs automates the complete 25-task grading scheme on sagittal MRI and, in the first hospital external validation of such a model, reduced severe-grade over-prediction under domain shift without loss of sensitivity -- supporting its role as a screening and triage aid.\n\nSummaryAn ordinal full-sequence multi-instance learning model graded all 25 lumbar MRI degeneration tasks and markedly reduced severe-grade false alarms in 60-patient hospital validation without loss of sensitivity.\n\nKey PointsO_LIFull-sequence attention was indispensable: balanced accuracy rose from 16.3% (single-slice baseline) to 51.4% over 24 sagittal slices. C_LIO_LIIn external validation, ordinal outputs reduced severe-grade predictions from 172 to 69 of 900 cells (McNemar p<0.0001) with comparable ranking ability (AUC 0.835 vs. 0.829). C_LIO_LIThe Moderate grade remained the hardest class across all conditions (AUC 0.70-0.78), tracking known inter-reader variability at the mild-moderate boundary. C_LI",
  "abstract_shown": true,
  "affiliations": [
    "Department of Orthopedics, The First Hospital of Lanzhou University, Lanzhou, China"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "S. Geng"
    },
    {
      "name": "Q. Miao"
    },
    {
      "name": "Q. Qin"
    },
    {
      "name": "H. Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "X. Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d8ajtcsmc36wq.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.20.26366005",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.20.26366005v1.full",
  "id": "d8ajtcsmc36wq",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.20.26366005v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "radiology and imaging"
    ],
    [
      "openalex",
      "2204|Medical Imaging and Analysis"
    ]
  ],
  "openalex": "W7223246022",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.20.26366005v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d8ajtcsmc36wq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:30Z",
      "native_id": "10.64898/2026.10.20.26366005",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.20.26366005v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Ordinal Multi-Instance Learning for Automated Multi-Task Grading of Lumbar Degenerative Conditions on Sagittal MRI, with External Validation",
  "title_text": "Ordinal Multi-Instance Learning for Automated Multi-Task Grading of Lumbar Degenerative Conditions on Sagittal MRI, with External Validation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:30Z",
  "url": "https://research.draconitic.com/paper/d8ajtcsmc36wq",
  "venue": "medRxiv",
  "versions": []
}